What’s Microsoft Agent Framework? [5 Minutes Overview]

Synthetic intelligence is altering shortly from easy chatbots to extra succesful autonomous brokers that exhibit reasoning, coordination, and execution of advanced duties. Microsoft has just lately made Agent Framework publicly accessible in public preview as an open-source SDK and runtime to ease the orchestration of multi-agent programs, an necessary step ahead for enterprises adopting agentic AI to alleviate fragmentation in tooling whereas now offering a bridge between experimenting and manufacturing.-grade deployment.

What’s Microsoft Agent Framework?

The Microsoft Agent Framework solves a key developer dilemma: selecting between cutting-edge AI analysis and steady, production-ready instruments. It unifies two frameworks:

• AutoGen: Brings superior multi-agent orchestration, permitting AIs to work collectively in advanced methods (e.g., group chats, debates).
• Semantic Kernel: Supplies the enterprise spine, together with safety, kind security, and telemetry.

This merger creates a singular platform the place you may construct a working AI agent in below 20 strains of code with out sacrificing the flexibility to create advanced, multi-agent workflows for industrial use.

Microsoft Agent Framework Structure

The core structure of this framework is comprised of 4 foundational components:

Open Requirements and Interoperability

The Microsoft Agent Framework is constructed on a precept of open requirements and interoperability, making certain brokers can talk throughout completely different platforms and combine seamlessly into current enterprise programs. It helps rising protocols to facilitate collaboration and simple software integration.

Key Options

• Cross-Platform Communication: Brokers can speak to one another throughout completely different runtimes utilizing Agent-to-Agent (A2A) protocols.
• Open Requirements: Helps MCP for real-time software connections and OpenAPI for easy REST API integration.
• Native Connectors: Contains built-in help for key providers like Azure AI Foundry, Microsoft Graph, SharePoint, Elasticsearch, and Redis.
• Structure Agnostic: Designed to work with Azure providers, third-party APIs, and customized inner programs with out vendor lock-in.

This method permits builders to plug AI brokers straight into their present know-how stack, bridging the hole between modern AI and established enterprise structure.

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Analysis-to-Manufacturing Pipeline

The framework gives a strong research-to-production pipeline, combining AutoGen’s superior orchestration patterns with the reliability required for enterprise use. This allows builders to handle advanced, multi-step enterprise processes via a structured and stateful workflow layer, which is important for prolonged operations.

This makes the framework excellent for reworking advanced enterprise processes into automated, multi-agent workflows.

Extensibility by Design

Microsoft Agent Framework provides a modular structure that helps agent configuration through the use of each declarative and programmatic types. Builders might outline brokers in YAML or JSON format so current versioning and collaborative improvement workflows make use of novel DevOps practices in defining brokers. Declaring agent definitions permits groups to handle agent definitions in model management alongside software code inside GitHub or Azure DevOps repositories. 

Pluggable reminiscence modules additionally permit a developer to retailer context and recall data via a number of back-end shops. Whether or not builders use in-memory storage for prototypes, Redis for eventualities with distributed brokers, or some type of proprietary vector database for semantic search, the framework works to supply context no matter structure.

Manufacturing-Prepared from Day One

The framework is engineered for enterprise adoption, integrating vital production-grade capabilities for observability, safety, and lifecycle administration straight into its core.

Key Manufacturing Options:

• Native Observability: Constructed-in OpenTelemetry integration gives full visibility into agent workflows, software utilization, and inter-agent collaboration, which is important for debugging, efficiency optimization, and compliance auditing.
• Enterprise-Grade Safety: Leverages Azure Entra ID for sturdy authentication and authorization, making certain all brokers function inside strict organizational safety insurance policies.
• Streamlined DevOps: Helps CI/CD pipelines via GitHub Actions and Azure DevOps, enabling groups to use a standardized software program improvement lifecycle to their AI brokers.

This built-in concentrate on governance and operational excellence ensures that multi-agent programs may be trusted, managed, and scaled successfully inside a real-world enterprise surroundings.

Getting Began with Agent Framework

For Python builders, set up is simple:

pip set up agent-framework --pre

For .NET builders:

dotnet add package deal Microsoft.Brokers.AI

Constructing Your First Agent

Let’s look at find out how to create a practical agent that may work together with instruments. Right here’s a Python instance that demonstrates the framework’s simplicity:

import asyncio
from agent_framework.azure import AzureOpenAIResponsesClient
from azure.identification import AzureCliCredential

# Outline a customized software perform
def calculate_discount(worth: float, discount_percent: float) -> float:
    """Calculate discounted worth"""
    return worth * (1 - discount_percent / 100)

async def predominant():
    # Initialize agent with Azure OpenAI
    agent = AzureOpenAIResponsesClient(
        credential=AzureCliCredential()
    ).create_agent(
        identify="ShoppingAssistant",
        directions="You assist prospects calculate costs and reductions.",
        instruments=[calculate_discount]  # Register the software
    )
    
    # Agent can now use the software robotically
    response = await agent.run(
        "If a laptop computer prices $1200 and has a 15% low cost, what is the closing worth?"
    )
    print(response)

asyncio.run(predominant())

The equal .NET implementation showcases comparable class:

utilizing Azure.AI.OpenAI;
utilizing Azure.Id;
utilizing Microsoft.Brokers.AI;

// Outline a software as a technique
static double CalculateDiscount(double worth, double discountPercent)
{
    return worth * (1 - discountPercent / 100);
}

var agent = new AzureOpenAIClient(
    new Uri(Atmosphere.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")!),
    new AzureCliCredential())
    .GetOpenAIResponseClient("gpt-4")
    .CreateAIAgent(
        identify: "ShoppingAssistant",
        directions: "You assist prospects calculate costs and reductions.",
        instruments: [CalculateDiscount]);

Console.WriteLine(await agent.RunAsync(
    "If a laptop computer prices $1200 and has a 15% low cost, what is the closing worth?"));

Multi-Agent Workflow Instance

For extra advanced eventualities, the framework helps orchestrating a number of specialised brokers. Right here’s a workflow that coordinates analysis and writing brokers:

from agent_framework.workflows import Workflow, WorkflowStep
from agent_framework.azure import AzureOpenAIResponsesClient

# Create specialised brokers
researcher = shopper.create_agent(
    identify="Researcher",
    directions="You analysis matters and supply factual data."
)

author = shopper.create_agent(
    identify="Author",
    directions="You write partaking articles based mostly on analysis."
)

# Outline workflow
workflow = Workflow(
    steps=[
        WorkflowStep(
            name="research",
            agent=researcher,
            output_variable="research_data"
        ),
        WorkflowStep(
            name="write",
            agent=writer,
            input_from="research_data",
            output_variable="article"
        )
    ]
)

# Execute workflow
end result = await workflow.run(
    input_data={"subject": "Way forward for Quantum Computing"}
)
print(end result["article"])

This workflow illustrates how the framework manages state amongst brokers, passing the researcher’s output as context to the author robotically. An inherent checkpoint system manages elapsed time to make sure the workflow can resume if something fails with out restarting and shedding what was beforehand finished.

Enterprise Adoption of Microsoft Agent Framework

A number of main organizations are already utilizing the Microsoft Agent Framework in real-world eventualities. Listed here are just a few examples:

• KPMG: Powering KPMG Clara AI, the framework connects specialised brokers to enterprise information and instruments with built-in security safeguards. Open-source connectors allow entry past Azure AI Foundry, supporting scalable multi-agent collaboration in globally regulated environments.
• Commerzbank: Exploring avatar-driven buyer help to ship pure, accessible, and regulation-compliant interactions.
• Citrix: Evaluating integration into digital desktop infrastructure to reinforce enterprise productiveness.
• Sitecore: Growing agent capabilities for entrepreneurs to automate workflows throughout the content material provide chain.

Voice Integration and Multi-Modal Capabilities

The Voice Dwell API is now usually accessible. It provides a unified, real-time speech-to-speech interface that mixes:

• Speech-to-text
• Generative AI fashions
• Textual content-to-speech
• Avatars
• Dialog enhancers

This low-latency stream helps voice-initiated and voice-concluded multi-agent workflows, making a extra pure consumer expertise.

Organizations utilizing Voice Dwell API embody:

• Capgemini: Customer support brokers
• healow: Studying tutors
• Astra Tech: HR assistants

These examples spotlight how the framework helps multi-modal agent experiences, extending past text-based interactions.

Addressing Enterprise Issues

Governance and Accountable AI

As AI adoption will increase, enterprises are putting better emphasis on accountable and compliant use of clever brokers. In accordance with McKinsey’s 2025 International AI Belief Survey, the greatest barrier to AI adoption is the absence of efficient governance and risk-management instruments.

Key Capabilities

• Brokers stay centered on their assigned goals and keep away from drifting into unintended duties or behaviors. This ensures operational consistency and reliability.
• These instruments defend towards immediate injection assaults and flag uncontrolled or dangerous agent actions for organizational assessment. This improves safety and oversight.
• Routinely detect when brokers entry Personally Identifiable Data (PII). This enables organizations to evaluate and refine their information dealing with insurance policies based mostly on entry patterns.
• All governance options are constructed into Azure AI Foundry. This gives a ready-to-use compliance layer that aligns with organizational insurance policies and regulatory requirements.
• These capabilities are important in sectors comparable to finance and healthcare, the place accountable AI use should be embedded all through the event and deployment lifecycle.

Developer Expertise: Staying in Movement

An trade examine exhibits that 50% of builders lose over ten hours per week on account of fragmented instruments and inefficient workflows. This productiveness drain impacts supply timelines and developer morale. The Microsoft Agent Framework addresses this problem by providing a unified improvement expertise that minimizes context switching and streamlines agent creation, testing, and deployment.

Key Advantages:

• Builders now not must toggle between terminals, logs, and dashboards. DevUI centralizes these duties, serving to groups keep centered and productive. 
• Builders can work regionally utilizing the AI Toolkit extension in Visible Studio Code, after which deploy to Azure AI Foundry with observability and compliance options enabled when wanted.
• The framework helps each Python and .NET, permitting groups to work of their most popular language whereas sustaining portability and consistency throughout environments.
• With standardized APIs, builders can collaborate throughout groups and languages while not having to study new interfaces, which improves effectivity and reduces onboarding time.
• The brand new DevUI gives an interactive interface that helps agent improvement, testing, and debugging. It enhances code-first workflows and simplifies fast prototyping and troubleshooting.

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Conclusion

The Microsoft Agent Framework is shaping the way forward for enterprise AI by merging innovation with governance, multi-modal capabilities, and developer-first tooling. It transforms experimentation into scalable, compliant options. As clever brokers grow to be central to enterprise workflows, this framework provides a dependable basis.

What are your ideas on adopting agentic AI in your group utilizing this framework? Let me know within the remark part under!




Information Science Trainee at Analytics Vidhya
I’m at present working as a Information Science Trainee at Analytics Vidhya, the place I concentrate on constructing data-driven options and making use of AI/ML strategies to resolve real-world enterprise issues. My work permits me to discover superior analytics, machine studying, and AI purposes that empower organizations to make smarter, evidence-based choices.
With a powerful basis in laptop science, software program improvement, and information analytics, I’m keen about leveraging AI to create impactful, scalable options that bridge the hole between know-how and enterprise.
📩 You may also attain out to me at [email protected]